Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression

Disease-associated loci share properties with response eQTLs under common environmental exposures

Many of the genetic loci associated with disease are expected to have context-dependent regulatory effects that are underrepresented in the transcriptomes of healthy, steady-state adult tissues. To understand gene regulation across diverse environmental conditions and cellular contexts, we treated a broad array of human cell types with three environmental exposures in vitro. With single-cell RNA-sequencing data from 1.4 million cells across 51 individuals, we identified hundreds of response expression quantitative loci (eQTLs) that are associated with inter-individual differences in regulatory changes following treatment with nicotine, caffeine, or ethanol in diverse cell types. We also identified dynamic regulatory effects that vary across differentiation trajectories in response to exposure. In contrast to steady-state eQTLs, and similar to disease risk loci, response eQTLs are enriched in distal enhancers and are regulating genes that experienced strong selective constraint, contain complex regulatory landscapes, and display diverse biological functions. We identified response eQTLs that coincide with disease-associated loci not explained by steady-state eQTLs. Our results highlight the complexity of genetic regulatory effects and suggest that our ability to interpret disease-associated loci will benefit from the pursuit of studies of gene-by-environment interactions in diverse biological contexts.

Systematic review and independent validation of genetic factors of radiographic outcome in rheumatoid arthritis identifies a genome-wide association with CARD9

OBJECTIVES

This study aimed to investigate non-HLA genetic mechanisms underlying radiographic severity in rheumatoid arthritis (RA).

METHODS

A systematic review of publications reporting non-HLA genetic associations with radiographic severity in RA across ancestries was undertaken. Experimental validation was performed in the Norfolk Arthritis Register, comprising 1407 patients with available genetic and treatment data followed prospectively for up to 10 years, with 2198 longitudinal radiographs. Genome-wide genotyping was performed with Illumina Human Core Exome Array. Radiographic outcomes (presence of erosions; Larsen score) were modelled longitudinally. Fine mapping and functional annotations to refine associations to potential causative loci were undertaken using FUMA, PolyPhen2, and RegulomeDB.

RESULTS

The systematic review identified 102 publications reporting 139 independent associations with radiographic outcome. Association with 15 independent polymorphisms were replicated in the Norfolk Arthritis Register data set, implicating adaptive immune processes (Th1, Th2, and Th17 pathways), cytokine regulation, and osteoclast differentiation. Notably, we refined the association of rs59902911 at the CARD9 locus to an intronic polymorphism within an active enhancer (rs78892335), achieving genome-wide significance and with an effect size exceeding the minimal clinically important difference for each copy of the minor allele (4.78 Larsen units/copy; 95% CI, 3.15-6.41; p = 9.01 × 10-9). This polymorphism is associated with the expression of CARD9 in immune cells, including B cells.

CONCLUSIONS

We provide a comprehensive list of validated genetic associations with RA outcome and demonstrate that non-HLA polymorphisms can associate with radiographic severity independently of disease susceptibility. This highlights the importance of dedicated genetic outcome studies for patient stratification in precision medicine for RA.

scMultiMap: Cell-type-specific mapping of enhancers and target genes from single-cell multimodal data

Mapping enhancers and target genes in disease-related cell types provides critical insights into the functional mechanisms of genome-wide association studies (GWAS) variants. Single-cell multimodal data, which measure gene expression and chromatin accessibility in the same cells, enable the cell-type-specific inference of enhancer-gene pairs. However, this task is challenged by high data sparsity, sequencing depth variation, and the computational burden of analyzing a large number of pairs. We introduce scMultiMap, a statistical method that infers enhancer-gene association from sparse multimodal counts using a joint latent-variable model. It adjusts for technical confounding, permits fast moment-based estimation and provides analytically derived p-values. In blood and brain data, scMultiMap shows appropriate type I error control, high statistical power, and computational efficiency (1% of existing methods). When applied to Alzheimer's disease (AD) data, scMultiMap gives the highest heritability enrichment in microglia and reveals insights into the regulatory mechanisms of AD GWAS variants.

Deciphering distinct genetic risk factors for FTLD-TDP pathological subtypes via whole-genome sequencing

Frontotemporal lobar degeneration with neuronal inclusions of the TAR DNA-binding protein 43 (FTLD-TDP) is a fatal neurodegenerative disorder with only a limited number of risk loci identified. We report our comprehensive genome-wide association study as part of the International FTLD-TDP Whole-Genome Sequencing Consortium, including 985 patients and 3,153 controls compiled from 26 institutions/brain banks in North America, Europe and Australia, and meta-analysis with the Dementia-seq cohort. We confirm UNC13A as the strongest overall FTLD-TDP risk factor and identify TNIP1 as a novel FTLD-TDP risk factor. In subgroup analyzes, we further identify genome-wide significant loci specific to each of the three main FTLD-TDP pathological subtypes (A, B and C), as well as enrichment of risk loci in distinct tissues, brain regions, and neuronal subtypes, suggesting distinct disease aetiologies in each of the subtypes. Rare variant analysis confirmed TBK1 and identified C3AR1, SMG8, VIPR1, RBPJL, L3MBTL1 and ANO9, as novel subtype-specific FTLD-TDP risk genes, further highlighting the role of innate and adaptive immunity and notch signaling pathway in FTLD-TDP, with potential diagnostic and novel therapeutic implications.

Genome-wide association study reveals a novel tuberculosis susceptibility locus in multiple East Asian and European populations

Background

Tuberculosis (TB) continues to be a leading cause of morbidity and mortality worldwide. Past genome-wide association studies (GWAS) have explored TB susceptibility across various ethnic groups, yet a significant portion of TB heritability remains unexplained.

Methods

We conducted GWAS in the Singapore Chinese and Vietnamese, followed by a comprehensive meta-analysis incorporating 4 independent East Asian datasets, resulting in a total of 11,841 cases and 197,373 population controls.

Findings

We identified a novel susceptibility locus for pulmonary TB (PTB) at 22q12.2 in East Asians [rs6006426, OR (95%Cl) =1.097(1.066, 1.130), P meta =3.31×10 -10 ]. The association was further validated in Europeans [OR (95%Cl) =1.101(1.002, 1.211), P =0.046] and was strengthened in the combined meta-anlaysis including 12,736 PTB cases and 673,864 controls [OR (95%Cl) =1.098(1.068, 1.129), P meta =4.33×10 -11 ]. rs6006426 affected SF3A1 expression in various immune cells ( P from 0.003 to 6.17×10 -18 ) and OSM expression in monocytes post lipopolysaccharide stimulation ( P =5.57×10 -4 ). CRISPR-Cas9 edited zebrafish embryos with osm depletion resulted in decreased burden of Mycobacterium marinum ( M.marinum ) in infected embryos ( P =0.047).

Interpretation

Our findings offer novel insights into the genetic factors underlying TB and reveals new avenues for understanding its etiology.

The identification of blood-derived response eQTLs reveals complex effects of regulatory variants on inflammatory and infectious disease risk

Hundreds of risk loci for immune mediated inflammatory and infectious diseases have been identified by genome-wide association studies (GWAS). Yet, what causal variants and genes in risk loci underpin the observed associations remains poorly understood for most. The identification of colocalized cis-expression Quantitative Trait Loci (cis-eQTLs) is a promising way to identify candidate causative genes. The catalogue of cis-eQTLs of the immune system is likely incomplete as many cis-eQTLs may be context-specific. We built a large cohort of 406 healthy individuals and expanded the immune cis-regulome through their whole blood transcriptome obtained after stimulation with specific toll-like receptor (TLR) agonists and T-cell receptor (TCR) antagonist. We report three mechanisms that may explain why an eQTL could only be revealed after immune stimulation. More than half of the cis-eQTLs detected in this study would have been overlooked without specific immune stimulations. We then mined this new catalogue of response (r)eQTLs, with public GWAS summary statistics of three diseases through a colocalization approach: inflammatory bowel diseases, rheumatoid arthritis and COVID-19 disease. We identified reQTL-specific colocalizations for risk loci for which no matching eQTL were reported before, revealing interesting new candidate causal genes.

Tracing the Evolution of Human Immunity Through Ancient DNA

Infections have imposed strong selection pressures throughout human evolution, making the study of natural selection's effects on immunity genes highly complementary to disease-focused research. This review discusses how ancient DNA studies, which have revolutionized evolutionary genetics, increase our understanding of the evolution of human immunity. These studies have shown that interbreeding between modern humans and Neanderthals or Denisovans has influenced present-day immune responses, particularly to viruses. Additionally, ancient genomics enables the tracking of how human immunity has evolved across cultural transitions, highlighting strong selection since the Bronze Age in Europe (<4,500 years) and potential genetic adaptations to epidemics raging during the Middle Ages and the European colonization of the Americas. Furthermore, ancient genomic studies suggest that the genetic risk for noninfectious immune disorders has gradually increased over millennia because alleles associated with increased risk for autoimmunity and inflammation once conferred resistance to infections. The challenge now is to extend these findings to diverse, non-European populations and to provide a more global understanding of the evolution of human immunity.

Probabilistic classification of gene-by-treatment interactions on molecular count phenotypes

Genetic variation can modulate response to treatment (G×T) or environmental stimuli (G×E), both of which can be highly consequential in biomedicine. An effective approach to identifying G×T signals and gaining insight into molecular mechanisms is mapping quantitative trait loci (QTL) of molecular count phenotypes, such as gene expression and chromatin accessibility, under multiple treatment conditions, which is termed response molecular QTL mapping. Although standard approaches evaluate the interaction between genetics and treatment conditions, they do not distinguish between meaningful interpretations such as whether a genetic effect is observed only in the treated condition or whether a genetic effect is observed always but accentuated in the treated condition. To address this gap, we have developed a downstream method for classifying response molecular QTLs into subclasses with meaningful genetic interpretations. Our method uses Bayesian model selection and assigns posterior probabilities to different types of G×T interactions for a given feature-SNP pair. We compare linear and nonlinear regression of log ⁡ -scale counts, noting that the latter accounts for an expected biological relationship between the genotype and the molecular count phenotype. Through simulation and application to existing datasets of molecular response QTLs, we show that our method provides an intuitive and well-powered framework to report and interpret G×T interactions. We provide a software package, ClassifyGxT [1].

Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms

BACKGROUND

Understanding the genetic causes underlying variability in chromatin accessibility can shed light on the molecular mechanisms through which genetic variants may affect complex traits. Thousands of ATAC-seq samples have been collected that hold information about chromatin accessibility across diverse cell types and contexts, but most of these are not paired with genetic information and come from distinct projects and laboratories.

RESULTS

We report here joint genotyping, chromatin accessibility peak calling, and discovery of quantitative trait loci which influence chromatin accessibility (caQTLs), demonstrating the capability of performing caQTL analysis on a large scale in a diverse sample set without pre-existing genotype information. Using 10,293 profiling samples representing 1454 unique donor individuals across 653 studies from public databases, we catalog 24,159 caQTLs in total. After joint discovery analysis, we cluster samples based on accessible chromatin profiles to identify context-specific caQTLs. We find that caQTLs are strongly enriched for annotations of gene regulatory elements across diverse cell types and tissues and are often linked with genetic variation associated with changes in expression (eQTLs), indicating that caQTLs can mediate genetic effects on gene expression. We demonstrate sharing of causal variants for chromatin accessibility across human traits, enabling a more complete picture of the genetic mechanisms underlying complex human phenotypes.

CONCLUSIONS

Our work provides a proof of principle for caQTL calling from previously ungenotyped samples and represents one of the largest, most diverse caQTL resources currently available, informing mechanisms of genetic regulation of gene expression and contribution to disease.

The Relationship Between CSF1R Signaling, Monocyte-Macrophage Differentiation, and Susceptibility to Inflammatory Bowel Disease

More than 300 genomic loci have been associated with increased susceptibility to inflammatory bowel disease (IBD) through genome-wide association studies. A major challenge in the translation of genome-wide association studies to mechanistic insights lies in connecting noncoding variants to function. For example, single-nucleotide variants (SNVs) in the vicinity of the gene encoding the transcription factor ETS2 on human chromosome 21 are associated with the risk of developing IBD in Europeans. The peak of SNV association lies within a distal enhancer that may regulate ETS2 transcription. The interpretation of this and many other SNV associations with IBD depends on a model linking variation in transcriptional regulation to the likelihood of developing chronic intestinal inflammation. One model for the ETS2 locus is that overexpression in monocytes is causally associated with the risk allele, which in turn leads to a hyperinflammatory state. Here we summarize evidence for an alternative mechanism focused on negative regulators of monocyte-macrophage activation. We argue that IBD susceptibility arises from dysregulation of monocyte adaptation in the intestinal milieu to form resident intestinal macrophages that are anergic to inflammatory stimuli. This process depends on signals initiated by macrophage colony-stimulating factor (CSF1) binding to its receptor (CSF1R). Within this framework, ETS2 is a myeloid-specific transcription factor, expressed in pluripotent and committed progenitors and monocytes, and is down-regulated by CSF1, in common with many genes associated with IBD susceptibility, including NOD2. ETS2 is also both a downstream target and a mediator of the CSF1/CSF1R signaling pathway. Therapeutic targeting of ETS2 and its upstream regulators has the potential to prevent CSF1-dependent monocyte differentiation toward a prorepair resident macrophage phenotype and consequently exacerbate intestinal inflammation.

Human longevity and Alzheimer's disease variants act via microglia and oligodendrocyte gene networks

Ageing underlies functional decline of the brain and is the primary risk factor for several neurodegenerative conditions, including Alzheimer's disease (AD). However, the molecular mechanisms that cause functional decline of the brain during ageing, and how these contribute to AD pathogenesis, are not well understood. The objective of this study was to identify biological processes that are altered during ageing in the hippocampus and that modify Ad risk and lifespan, and then to identify putative gene drivers of these programmes. We integrated common human genetic variation associated with human lifespan or Ad from genome-wide association studies with co-expression transcriptome networks altered with age in the mouse and human hippocampus. Our work confirmed that genetic variation associated with Ad was enriched in gene networks expressed by microglia responding to ageing and revealed that they were also enriched in an oligodendrocytic gene network. Compellingly, longevity-associated genetic variation was enriched in a gene network expressed by homeostatic microglia whose expression declined with age. The genes driving this enrichment include CASP8 and STAT3, highlighting a potential role for these longevity-associated genes in the homeostatic functions of innate immune cells, and these genes might drive 'inflammageing'. Thus, we observed that gene variants contributing to ageing and AD balance different aspects of microglial and oligodendrocytic function. Furthermore, we also highlight putative Ad risk genes, such as LAPTM5, ITGAM and LILRB4, whose association with Ad falls below genome-wide significance but show strong co-expression with known Ad risk genes in these networks. Indeed, five of the putative risk genes highlighted by our analysis, ANKH, GRN, PLEKHA1, SNX1 and UNC5CL, have subsequently been identified as genome-wide significant risk genes in a subsequent genome-wide association study with larger sample size, validating our analysis. This work identifies new genes that influence ageing and AD pathogenesis, and highlights the importance of microglia and oligodendrocytes in the resilience of the brain against ageing and AD pathogenesis. Our findings have implications for developing markers indicating the physiological age of the brain and new targets for therapeutic intervention.

Single-cell multiomics of neuronal activation reveals context-dependent genetic control of brain disorders

Despite hundreds of genetic risk loci identified for neuropsychiatric disorders (NPD), most causal variants/genes remain unknown. A major hurdle is that disease risk variants may act in specific biological contexts, e.g., during neuronal activation, which is difficult to study in vivo at the population level. Here, we conducted a single-cell multiomics study of neuronal activation (stimulation) in human iPSC-induced excitatory and inhibitory neurons from 100 donors, and uncovered abundant neuronal stimulation-specific causal variants/genes for NPD. We surveyed NPD-relevant transcriptomic and epigenomic landscape of neuronal activation and identified thousands of genetic variants associated with activity-dependent gene expression (i.e., eQTL) and chromatin accessibility (i.e., caQTL). These caQTL explained considerably larger proportions of NPD heritability than the eQTL. Integrating the multiomic data with GWAS further revealed NPD risk variants/genes whose effects were only detected upon stimulation. Interestingly, multiple lines of evidence support a role of activity-dependent cholesterol metabolism in NPD. Our work highlights the power of cell stimulation to reveal context-dependent "hidden" genetic effects.

Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data

Existing methods for gene regulatory network (GRN) inference rely on gene expression data alone or on lower resolution bulk data. Despite the recent integration of chromatin accessibility and RNA sequencing data, learning complex mechanisms from limited independent data points still presents a daunting challenge. Here we present LINGER (Lifelong neural network for gene regulation), a machine-learning method to infer GRNs from single-cell paired gene expression and chromatin accessibility data. LINGER incorporates atlas-scale external bulk data across diverse cellular contexts and prior knowledge of transcription factor motifs as a manifold regularization. LINGER achieves a fourfold to sevenfold relative increase in accuracy over existing methods and reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Following the GRN inference from reference single-cell multiome data, LINGER enables the estimation of transcription factor activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies.

Low expression of the CCL5 gene and low serum concentrations of CCL5 in severe invasive group a streptococcal disease

PURPOSE

Our objective was to elucidate host dependent factors of disease severity in invasive group A Streptococcal disease (iGAS) using transcriptome profiling of iGAS cases of varying degrees of severity at different timepoints. To our knowledge there are no previous transcriptome studies in iGAS patients.

METHODS

We recruited iGAS cases from June 2018 to July 2020. Whole blood samples for transcriptome analysis and serum for biomarker analysis were collected at three timepoints representing the acute (A), the convalescent (B) and the post-infection phase (C). Gene expression was compared against clinical traits and disease course. Serum chemokine ligand 5 (CCL5, an inflammatory cytokine) concentration was also measured.

RESULTS

Forty-five patients were enrolled. After disqualifying degraded or impure RNAs we had 34, 31 and 21 subjects at timepoints A, B, and C, respectively. Low expression of the CCL5 gene correlated strongly with severity (death or need for intensive care) at timepoint A (AUC = 0.92), supported by low concentrations of CCL5 in sera.

CONCLUSIONS

Low gene expression levels and low serum concentration of CCL5 in the early stages of an iGAS infection were associated with a more severe disease course. CCL5 might have potential as a predictor of disease severity. Low expression of genes of cytotoxic immunity, especially CCL5, and corresponding low serum concentrations of CCL5 associated with a severe disease course, i.e. death, or need for intensive care, in early phase of invasive group A Streptococcal disease.

Genetic variation in IL-4 activated tissue resident macrophages determines strain-specific synergistic responses to LPS epigenetically

How macrophages in the tissue environment integrate multiple stimuli depends on the genetic background of the host, but this is still poorly understood. We investigate IL-4 activation of male C57BL/6 and BALB/c strain specific in vivo tissue-resident macrophages (TRMs) from the peritoneal cavity. C57BL/6 TRMs are more transcriptionally responsive to IL-4 stimulation, with induced genes associated with more super enhancers, induced enhancers, and topologically associating domains (TAD) boundaries. IL-4-directed epigenomic remodeling reveals C57BL/6 specific enrichment of NF-κB, IRF, and STAT motifs. Additionally, IL-4-activated C57BL/6 TRMs demonstrate an augmented synergistic response upon in vitro lipopolysaccharide (LPS) exposure, despite naïve BALB/c TRMs displaying a more robust transcriptional response to LPS. Single-cell RNA sequencing (scRNA-seq) analysis of mixed bone marrow chimeras indicates that transcriptional differences and synergy are cell intrinsic within the same tissue environment. Hence, genetic variation alters IL-4-induced cell intrinsic epigenetic reprogramming resulting in strain specific synergistic responses to LPS exposure.

Elucidating the Role of Estrogen Effects in Leukemia: Insights from Single-Cell RNA Sequencing and Mendelian Randomization

Background: Epidemiological studies have confirmed the potential role of estrogen effects in influencing the development and outcome of leukemia. Estrogen effects are increasingly attracting research interest for their potential antitumor effects beyond gynecological tumors. However, their causal relationship remains unclear. Methods: In a novel approach, this study integrates single-cell RNA sequencing (scRNA-seq) with Mendelian randomization (MR) to explore the relationship between estrogen (and its receptor) and leukemia (and its related proteins). This integration showcases the uniqueness of our methodology and provides a new perspective for understanding the molecular relationship between them. Secondary analyses using genetic risk scores (GRS) were performed to further verify the robustness of the results. Results: Our scRNA-seq analysis identified 14 BMMC mononuclear cell subsets, and the result showed that the estrogen receptor was implicated in leukemia. The MR results showed that there was a relationship between estradiol and leukemia inhibitory factor (β = 0.0621; P = 0.0229), and leukemia inhibitory factor receptor (β = 0.0665; P = 0.0218). The result of GRS analysis verified the MR analysis. Conclusions: While both scRNA-seq and MR have yielded intriguing results, inconsistencies between these methodologies hint at a more elaborate underlying mechanism. The observed discrepancies underscore the complexity of the estrogen effects-leukemia relationship, suggesting that elucidating these interactions demands larger cohorts and enhanced sequencing depth in future studies. This research paves the way for a more nuanced understanding of the role of estrogen effects in leukemia and sets the stage for targeted therapeutic interventions.

A modeling framework for detecting and leveraging node-level information in Bayesian network inference

Bayesian graphical models are powerful tools to infer complex relationships in high dimension, yet are often fraught with computational and statistical challenges. If exploited in a principled way, the increasing information collected alongside the data of primary interest constitutes an opportunity to mitigate these difficulties by guiding the detection of dependence structures. For instance, gene network inference may be informed by the use of publicly available summary statistics on the regulation of genes by genetic variants. Here we present a novel Gaussian graphical modeling framework to identify and leverage information on the centrality of nodes in conditional independence graphs. Specifically, we consider a fully joint hierarchical model to simultaneously infer (i) sparse precision matrices and (ii) the relevance of node-level information for uncovering the sought-after network structure. We encode such information as candidate auxiliary variables using a spike-and-slab submodel on the propensity of nodes to be hubs, which allows hypothesis-free selection and interpretation of a sparse subset of relevant variables. As efficient exploration of large posterior spaces is needed for real-world applications, we develop a variational expectation conditional maximization algorithm that scales inference to hundreds of samples, nodes and auxiliary variables. We illustrate and exploit the advantages of our approach in simulations and in a gene network study which identifies hub genes involved in biological pathways relevant to immune-mediated diseases.

Widespread gene-environment interactions shape the immune response to SARS-CoV-2 infection in hospitalized COVID-19 patients

Genome-wide association studies performed in patients with coronavirus disease 2019 (COVID-19) have uncovered various loci significantly associated with susceptibility to SARS-CoV-2 infection and COVID-19 disease severity. However, the underlying cis-regulatory genetic factors that contribute to heterogeneity in the response to SARS-CoV-2 infection and their impact on clinical phenotypes remain enigmatic. Here, we used single-cell RNA-sequencing to quantify genetic contributions to cis-regulatory variation in 361,119 peripheral blood mononuclear cells across 63 COVID-19 patients during acute infection, 39 samples collected in the convalescent phase, and 106 healthy controls. Expression quantitative trait loci (eQTL) mapping across cell types within each disease state group revealed thousands of cis-associated variants, of which hundreds were detected exclusively in immune cells derived from acute COVID-19 patients. Patient-specific genetic effects dissipated as infection resolved, suggesting that distinct gene regulatory networks are at play in the active infection state. Further, 17.2% of tested loci demonstrated significant cell state interactions with genotype, with pathways related to interferon responses and oxidative phosphorylation showing pronounced cell state-dependent variation, predominantly in CD14+ monocytes. Overall, we estimate that 25.6% of tested genes exhibit gene-environment interaction effects, highlighting the importance of environmental modifiers in the transcriptional regulation of the immune response to SARS-CoV-2. Our findings underscore the importance of expanding the study of regulatory variation to relevant cell types and disease contexts and argue for the existence of extensive gene-environment effects among patients responding to an infection.

Expression quantitative trait loci influence DNA damage-induced apoptosis in cancer

BACKGROUND

Genomic instability and evading apoptosis are two fundamental hallmarks of cancer and closely linked to DNA damage response (DDR). By analyzing expression quantitative trait loci (eQTL) upon cell stimulation (called exposure eQTL (e2QTL)) it is possible to identify context specific gene regulatory variants and connect them to oncological diseases based on genome-wide association studies (GWAS).

RESULTS

We isolate CD8+ T cells from 461 healthy donors and stimulate them with high doses of 5 different carcinogens to identify regulatory mechanisms of DNA damage-induced apoptosis. Across all stimuli, we find 5,373 genes to be differentially expressed, with 85% to 99% of these genes being suppressed. While upregulated genes are specific to distinct stimuli, downregulated genes are shared across conditions but exhibit enrichment in biological processes depending on the DNA damage type. Analysis of eQTL reveals 654 regulated genes across conditions. Among them, 47 genes are significant e2QTL, representing a fraction of 4% to 5% per stimulus. To unveil disease relevant genetic variants, we compare eQTL and e2QTL with GWAS risk variants. We identify gene regulatory variants for KLF2, PIP4K2A, GPR160, RPS18, ARL17B and XBP1 that represent risk variants for oncological diseases.

CONCLUSION

Our study highlights the relevance of gene regulatory variants influencing DNA damage-induced apoptosis in cancer. The results provide new insights in cellular mechanisms and corresponding genes contributing to inter-individual effects in cancer development.

Genotype-by-environment interactions shape ubiquitin-proteasome system activity

In genotype-by-environment interactions (GxE), the effect of a genetic variant on a trait depends on the environment. GxE influences numerous organismal traits across eukaryotic life. However, we have a limited understanding of how GxE shapes the molecular processes that give rise to organismal traits. Here, we characterized how GxE shapes protein degradation, an essential molecular process that influences numerous aspects of cellular and organismal physiology. Using the yeast Saccharomyces cerevisiae, we characterized GxE in the activity of the ubiquitin-proteasome system (UPS), the primary protein degradation system in eukaryotes. By mapping genetic influences on the degradation of six substrates that engage multiple distinct UPS pathways across eight diverse environments, we discovered extensive GxE in the genetics of UPS activity. Hundreds of locus effects on UPS activity varied depending on the substrate, the environment, or both. Most of these cases corresponded to loci that were present in one environment but not another ("presence / absence" GxE), while a smaller number of loci had opposing effects in different environments ("sign change" GxE). The number of loci exhibiting GxE, their genomic location, and the type of GxE (presence / absence or sign change) varied across UPS substrates. Loci exhibiting GxE were clustered at genomic regions that contain core UPS genes and especially at regions containing variation that affects the expression of thousands of genes, suggesting indirect contributions to UPS activity. Our results reveal highly complex interactions at the level of substrates and environments in the genetics of protein degradation.

Fine-mapping and molecular characterisation of primary sclerosing cholangitis genetic risk loci

Genome-wide association studies of primary sclerosing cholangitis have identified 23 susceptibility loci. The majority of these loci reside in non-coding regions of the genome and are thought to exert their effect by perturbing the regulation of nearby genes. Here, we aim to identify these genes to improve the biological understanding of primary sclerosing cholangitis, and nominate potential drug targets. We first build an eQTL map for six primary sclerosing cholangitis-relevant T-cell subsets obtained from the peripheral blood of primary sclerosing cholangitis and ulcerative colitis patients. These maps identify 10,459 unique eGenes, 87% of which are shared across all six primary sclerosing cholangitis T-cell types. We then search for colocalisations between primary sclerosing cholangitis loci and eQTLs and undertake Bayesian fine-mapping to identify disease-causing variants. In this work, colocalisation analyses nominate likely primary sclerosing cholangitis effector genes and biological mechanisms at five non-coding (UBASH3A, PRKD2, ETS2 and AP003774.1/CCDC88B) and one coding (SH2B3) primary sclerosing cholangitis loci. Through fine-mapping we identify likely causal variants for a third of all primary sclerosing cholangitis-associated loci, including two to single variant resolution.

Immunocyte phenotype and breast cancer risk: A Mendel randomization analysis

BACKGROUND

Breast cancer remains a significant global health challenge. Understanding its etiological factors, particularly the role of immune system components, is crucial. This study leverages Mendelian randomization (MR) to investigate the causal relationship between various immune cell features and the risk of developing breast cancer.

METHODS

Utilizing two-sample MR analysis, we examined 731 immune cell features across 7 groups for their potential causal links to breast cancer. We analyzed genome-wide association studies (GWAS) data of 257,730 Europeans, comprising 17,389 cases and 240,341 controls, focusing on 24,133,589 single nucleotide polymorphisms (SNPs). Instrumental variables (IVs) were selected based on genetic associations, with rigorous statistical methods employed, including inverse variance weighting (IVW) and weighted median-based estimation.

RESULTS

Our analysis identified 20 immunophenotypes with significant causal associations with breast cancer risk. Notably, contain B cell, mature T cell, T + B + NK (TBNK) cells, regulatory T (Treg) cell, Classic dendritic cells (cDCs), Monocyte, and Myeloid cell group features displayed positive or negative correlations with breast cancer. For instance, specific B cell phenotypes were found to have both positive and negative causal relationships with breast cancer. Additionally, reverse MR analysis revealed no significant causal effects of breast cancer on these immune characteristics.

CONCLUSIONS

This study underscores the complex interplay between various immune cell phenotypes and breast cancer risk. The identified immunophenotypes could be potential biomarkers or targets for future therapeutic interventions. Our findings contribute to a deeper understanding of the immunological dimensions of breast cancer etiology.

FAM13A polymorphism is associated with a usual interstitial pneumonia pattern in patients with systemic sclerosis-associated interstitial lung disease

OBJECTIVES

The MUC5B promoter single nucleotide polymorphism (SNP) rs35705950 has been associated with idiopathic pulmonary fibrosis (IPF) and rheumatoid arthritis (RA)-related interstitial lung disease (ILD), but not with systemic sclerosis (SSc)-ILD. We hypothesized that the MUC5B promoter polymorphism or other IPF susceptibility loci are associated with an increased risk for the uncommon SSc-usual interstitial pneumonia (UIP) endophenotype, rather than SSc-ILD in general.

METHODS

We performed a cross-sectional study of SSc-ILD patients from 4 US Scleroderma Programs to investigate the frequency of MUC5B rs35705950 and 12 additional IPF susceptibility loci. SSc-ILD patients were stratified by high resolution chest CT (HRCT) imaging findings into UIP and non-UIP groups. Analysis of HRCTs performed by a thoracic radiologist blinded to participants' characteristics classified each scan as definite UIP, probable UIP, indeterminate, or alternative diagnosis, according to American Thoracic Society criteria.

RESULTS

Four-hundred eighty-nine SSc-ILD patients were included; 80% were female and 75% were White. Twenty-three (4.7%) patients had a definite UIP pattern. The MUC5B SNP rs35705950 was not associated with a definite UIP pattern in SSc-ILD. In contrast, patients carrying 2 copies of the IPF risk gene FAM13A minor allele rs2609255 had significantly higher odds of a definite UIP pattern compared with the other patterns (OR 3.40, 95% CI 1.19-9.70), and compared with an alternative diagnosis (OR 3.65, 95% CI 1.25-10.65).

CONCLUSION

We demonstrated a novel association between FAM13A and SSc-UIP. Contrary to IPF and RA-ILD, the MUC5B promoter polymorphism was not associated with a definite UIP pattern in SSc-ILD.

Extensive co-regulation of neighboring genes complicates the use of eQTLs in target gene prioritization

Identifying causal genes underlying genome-wide association studies (GWASs) is a fundamental problem in human genetics. Although colocalization with gene expression quantitative trait loci (eQTLs) is often used to prioritize GWAS target genes, systematic benchmarking has been limited due to unavailability of large ground truth datasets. Here, we re-analyzed plasma protein QTL data from 3,301 individuals of the INTERVAL cohort together with 131 eQTL Catalog datasets. Focusing on variants located within or close to the affected protein identified 793 proteins with at least one cis-pQTL where we could assume that the most likely causal gene was the gene coding for the protein. We then benchmarked the ability of cis-eQTLs to recover these causal genes by comparing three Bayesian colocalization methods (coloc.susie, coloc.abf, and CLPP) and five Mendelian randomization (MR) approaches (three varieties of inverse-variance weighted MR, MR-RAPS, and MRLocus). We found that assigning fine-mapped pQTLs to their closest protein coding genes outperformed all colocalization methods regarding both precision (71.9%) and recall (76.9%). Furthermore, the colocalization method with the highest recall (coloc.susie - 46.3%) also had the lowest precision (45.1%). Combining evidence from multiple conditionally distinct colocalizing QTLs with MR increased precision to 81%, but this was accompanied by a large reduction in recall to 7.1%. Furthermore, the choice of the MR method greatly affected performance, with the standard inverse-variance-weighted MR often producing many false positives. Our results highlight that linking GWAS variants to target genes remains challenging with eQTL evidence alone, and prioritizing novel targets requires triangulation of evidence from multiple sources.

Cell-type-specific mapping of enhancers and target genes from single-cell multimodal data

Mapping enhancers and target genes in disease-related cell types has provided critical insights into the functional mechanisms of genetic variants identified by genome-wide association studies (GWAS). However, most existing analyses rely on bulk data or cultured cell lines, which may fail to identify cell-type-specific enhancers and target genes. Recently, single-cell multimodal data measuring both gene expression and chromatin accessibility within the same cells have enabled the inference of enhancer-gene pairs in a cell-type-specific and context-specific manner. However, this task is challenged by the data's high sparsity, sequencing depth variation, and the computational burden of analyzing a large number of enhancer-gene pairs. To address these challenges, we propose scMultiMap, a statistical method that infers enhancer-gene association from sparse multimodal counts using a joint latent-variable model. It adjusts for technical confounding, permits fast moment-based estimation and provides analytically derived p -values. In systematic analyses of blood and brain data, scMultiMap shows appropriate type I error control, high statistical power with greater reproducibility across independent datasets and stronger consistency with orthogonal data modalities. Meanwhile, its computational cost is less than 1% of existing methods. When applied to single-cell multimodal data from postmortem brain samples from Alzheimer's disease (AD) patients and controls, scMultiMap gave the highest heritability enrichment in microglia and revealed new insights into the regulatory mechanisms of AD GWAS variants in microglia.

Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms

Background

Understanding the genetic causes for variability in chromatin accessibility can shed light on the molecular mechanisms through which genetic variants may affect complex traits. Thousands of ATAC-seq samples have been collected that hold information about chromatin accessibility across diverse cell types and contexts, but most of these are not paired with genetic information and come from diverse distinct projects and laboratories.

Results

We report here joint genotyping, chromatin accessibility peak calling, and discovery of quantitative trait loci which influence chromatin accessibility (caQTLs), demonstrating the capability of performing caQTL analysis on a large scale in a diverse sample set without pre-existing genotype information. Using 10,293 profiling samples representing 1,454 unique donor individuals across 653 studies from public databases, we catalog 23,381 caQTLs in total. After joint discovery analysis, we cluster samples based on accessible chromatin profiles to identify context-specific caQTLs. We find that caQTLs are strongly enriched for annotations of gene regulatory elements across diverse cell types and tissues and are often strongly linked with genetic variation associated with changes in expression (eQTLs), indicating that caQTLs can mediate genetic effects on gene expression. We demonstrate sharing of causal variants for chromatin accessibility and diverse complex human traits, enabling a more complete picture of the genetic mechanisms underlying complex human phenotypes.

Conclusions

Our work provides a proof of principle for caQTL calling from previously ungenotyped samples, and represents one of the largest, most diverse caQTL resources currently available, informing mechanisms of genetic regulation of gene expression and contribution to disease.

Oxygen-induced stress reveals context-specific gene regulatory effects in human brain organoids

The interaction between genetic variants and environmental stressors is key to understanding the mechanisms underlying neurological diseases. In this study, we used human brain organoids to explore how varying oxygen levels expose context-dependent gene regulatory effects. By subjecting a genetically diverse panel of 21 brain organoids to hypoxic and hyperoxic conditions, we identified thousands of gene regulatory changes that are undetectable under baseline conditions, with 1,745 trait-associated genes showing regulatory effects only in response to oxygen stress. To capture more nuanced transcriptional patterns, we employed topic modeling, which revealed context-specific gene regulation linked to dynamic cellular processes and environmental responses, offering a deeper understanding of how gene regulation is modulated in the brain. These findings underscore the importance of genotype-environment interactions in genetic studies of neurological disorders and provide new insights into the hidden regulatory mechanisms influenced by environmental factors in the brain.

The Genetics of IgA Nephropathy: Implications for Future Therapies

IgA nephropathy (IgAN), the most prevalent primary glomerulonephritis worldwide, carries a considerable lifetime risk of kidney failure. The etiology of IgAN, however, remains incompletely understood, and effective treatment is lacking. Although the multihit model effectively identifies key steps in IgAN development and, to date, provides the best description of IgAN pathogenesis, it remains under development to fully capture the complexity of immune system dysregulation. Large-scale genome-wide association studies have revealed clues regarding the association between IgAN and genes in both innate and adaptive immune pathways. Hence, genetic investigations may shed light on the aberrant molecular mechanisms, thereby presenting new opportunities for therapeutic advancements. This review discusses the genetic associations that have been robustly connected with IgAN, placing them within the framework of disease mechanism. Altogether, these findings highlight numerous new possibilities for the development of treatments and the road to personalized medicine.

The Genetic Determinants and Genomic Consequences of Non-Leukemogenic Somatic Point Mutations

Clonal hematopoiesis (CH) is defined by the expansion of a lineage of genetically identical cells in blood. Genetic lesions that confer a fitness advantage, such as point mutations or mosaic chromosomal alterations (mCAs) in genes associated with hematologic malignancy, are frequent mediators of CH. However, recent analyses of both single cell-derived colonies of hematopoietic cells and population sequencing cohorts have revealed CH frequently occurs in the absence of known driver genetic lesions. To characterize CH without known driver genetic lesions, we used 51,399 deeply sequenced whole genomes from the NHLBI TOPMed sequencing initiative to perform simultaneous germline and somatic mutation analyses among individuals without leukemogenic point mutations (LPM), which we term CH-LPMneg. We quantified CH by estimating the total mutation burden. Because estimating somatic mutation burden without a paired-tissue sample is challenging, we developed a novel statistical method, the Genomic and Epigenomic informed Mutation (GEM) rate, that uses external genomic and epigenomic data sources to distinguish artifactual signals from true somatic mutations. We performed a genome-wide association study of GEM to discover the germline determinants of CH-LPMneg. After fine-mapping and variant-to-gene analyses, we identified seven genes associated with CH-LPMneg (TCL1A, TERT, SMC4, NRIP1, PRDM16, MSRA, SCARB1), and one locus associated with a sex-associated mutation pathway (SRGAP2C). We performed a secondary analysis excluding individuals with mCAs, finding that the genetic architecture was largely unaffected by their inclusion. Functional analyses of SMC4 and NRIP1 implicated altered HSC self-renewal and proliferation as the primary mediator of mutation burden in blood. We then performed comprehensive multi-tissue transcriptomic analyses, finding that the expression levels of 404 genes are associated with GEM. Finally, we performed phenotypic association meta-analyses across four cohorts, finding that GEM is associated with increased white blood cell count and increased risk for incident peripheral artery disease, but is not significantly associated with incident stroke or coronary disease events. Overall, we develop GEM for quantifying mutation burden from WGS without a paired-tissue sample and use GEM to discover the genetic, genomic, and phenotypic correlates of CH-LPMneg.

The pathogenesis of gout: molecular insights from genetic, epigenomic and transcriptomic studies

The pathogenesis of gout involves a series of steps beginning with hyperuricaemia, followed by the deposition of monosodium urate crystal in articular structures and culminating in an innate immune response, mediated by the NLRP3 inflammasome, to the deposited crystals. Large genome-wide association studies (GWAS) of serum urate levels initially identified the genetic variants with the strongest effects, mapping mainly to genes that encode urate transporters in the kidney and gut. Other GWAS highlighted the importance of uncommon genetic variants. More recently, genetic and epigenetic genome-wide studies have revealed new pathways in the inflammatory process of gout, including genetic associations with epigenomic modifiers. Epigenome-wide association studies are also implicating epigenomic remodelling in gout, which perhaps regulates the responsiveness of the innate immune system to monosodium urate crystals. Notably, genes implicated in gout GWAS do not include those encoding components of the NLRP3 inflammasome itself, but instead include genes encoding molecules involved in its regulation. Knowledge of the molecular mechanisms underlying gout has advanced through the translation of genetic associations into specific molecular mechanisms. Notable examples include ABCG2, HNF4A, PDZK1, MAF and IL37. Current genetic studies are dominated by participants of European ancestry; however, studies focusing on other population groups are discovering informative population-specific variants associated with gout.

Scalable genetic screening for regulatory circuits using compressed Perturb-seq

Pooled CRISPR screens with single-cell RNA sequencing readout (Perturb-seq) have emerged as a key technique in functional genomics, but they are limited in scale by cost and combinatorial complexity. In this study, we modified the design of Perturb-seq by incorporating algorithms applied to random, low-dimensional observations. Compressed Perturb-seq measures multiple random perturbations per cell or multiple cells per droplet and computationally decompresses these measurements by leveraging the sparse structure of regulatory circuits. Applied to 598 genes in the immune response to bacterial lipopolysaccharide, compressed Perturb-seq achieves the same accuracy as conventional Perturb-seq with an order of magnitude cost reduction and greater power to learn genetic interactions. We identified known and novel regulators of immune responses and uncovered evolutionarily constrained genes with downstream targets enriched for immune disease heritability, including many missed by existing genome-wide association studies. Our framework enables new scales of interrogation for a foundational method in functional genomics.

Integrating the Thrifty Genotype and Evolutionary Mismatch Hypotheses to understand variation in cardiometabolic disease risk

More than 60 years ago, James Neel proposed the Thrifty Genotype Hypothesis to explain the widespread prevalence of type 2 diabetes in Western, industrial contexts. This hypothesis posits that variants linked to conservative energy usage and increased fat deposition would have been favored throughout human evolution due to the advantages they could provide during periods of resource limitation. However, in industrial environments, these variants instead produce an increased risk of obesity, metabolic syndrome, type 2 diabetes, and related health issues. This hypothesis has been popular and impactful, with thousands of citations, many ongoing debates, and several spin-off theories in biomedicine, evolutionary biology, and anthropology. However, despite great attention, the applicability and utility of the Thrifty Genotype Hypothesis (TGH) to modern human health remains, in our opinion, unresolved. To move research in this area forward, we first discuss the original formulation of the TGH and its critiques. Second, we trace the TGH to updated hypotheses that are currently at the forefront of the evolutionary medicine literature-namely, the Evolutionary Mismatch Hypothesis. Third, we lay out empirical predictions for updated hypotheses and evaluate them against the current literature. Finally, we discuss study designs that could be fruitful for filling current knowledge gaps; here, we focus on partnerships with subsistence-level groups undergoing lifestyle transitions, and we present data from an ongoing study with the Orang Asli of Malaysia to illustrate this point. Overall, we hope this synthesis will guide new empirical research aimed at understanding how the human evolutionary past interacts with our modern environments to influence cardiometabolic health.

New insight into the CNC-bZIP member, NFE2L3, in human diseases

Nuclear factor erythroid 2 (NF-E2)-related factor 3 (NFE2L3), a member of the CNC-bZIP subfamily and widely found in a variety of tissues, is an endoplasmic reticulum (ER) membrane-anchored transcription factor that can be released from the ER and moved into the nucleus to bind the promoter region to regulate a series of target genes involved in antioxidant, inflammatory responses, and cell cycle regulation in response to extracellular or intracellular stress. Recent research, particularly in the past 5 years, has shed light on NFE2L3's participation in diverse biological processes, including cell differentiation, inflammatory responses, lipid homeostasis, immune responses, and tumor growth. Notably, NFE2L3 has been identified as a key player in the development and prognosis of multiple cancers including colorectal cancer, thyroid cancer, breast cancer, hepatocellular carcinoma, gastric cancer, renal cancer, bladder cancer, esophageal squamous cell carcinoma, T cell lymphoblastic lymphoma, pancreatic cancer, and squamous cell carcinoma. Furthermore, research has linked NFE2L3 to other cancers such as lung adenocarcinoma, malignant pleural mesothelioma, ovarian cancer, glioblastoma multiforme, and laryngeal carcinoma, indicating its potential as a target for innovative cancer treatment approaches. Therefore, to gain a better understanding of the role of NFE2L3 in disease, this review offers insights into the discovery, structure, function, and recent advancements in the study of NFE2L3 to lay the groundwork for the development of NFE2L3-targeted cancer therapies.

Deciphering Distinct Genetic Risk Factors for FTLD-TDP Pathological Subtypes via Whole-Genome Sequencing

Frontotemporal lobar degeneration with neuronal inclusions of the TAR DNA-binding protein 43 (FTLD-TDP) is a fatal neurodegenerative disorder with only a limited number of risk loci identified. We report our comprehensive genome-wide association study as part of the International FTLD-TDP Whole-Genome Sequencing Consortium, including 985 cases and 3,153 controls, and meta-analysis with the Dementia-seq cohort, compiled from 26 institutions/brain banks in the United States, Europe and Australia. We confirm UNC13A as the strongest overall FTLD-TDP risk factor and identify TNIP1 as a novel FTLD-TDP risk factor. In subgroup analyses, we further identify for the first time genome-wide significant loci specific to each of the three main FTLD-TDP pathological subtypes (A, B and C), as well as enrichment of risk loci in distinct tissues, brain regions, and neuronal subtypes, suggesting distinct disease aetiologies in each of the subtypes. Rare variant analysis confirmed TBK1 and identified VIPR1 , RBPJL , and L3MBTL1 as novel subtype specific FTLD-TDP risk genes, further highlighting the role of innate and adaptive immunity and notch signalling pathway in FTLD-TDP, with potential diagnostic and novel therapeutic implications.

A disease-associated gene desert directs macrophage inflammation through ETS2

Increasing rates of autoimmune and inflammatory disease present a burgeoning threat to human health1. This is compounded by the limited efficacy of available treatments1 and high failure rates during drug development2, highlighting an urgent need to better understand disease mechanisms. Here we show how functional genomics could address this challenge. By investigating an intergenic haplotype on chr21q22-which has been independently linked to inflammatory bowel disease, ankylosing spondylitis, primary sclerosing cholangitis and Takayasu's arteritis3-6-we identify that the causal gene, ETS2, is a central regulator of human inflammatory macrophages and delineate the shared disease mechanism that amplifies ETS2 expression. Genes regulated by ETS2 were prominently expressed in diseased tissues and more enriched for inflammatory bowel disease GWAS hits than most previously described pathways. Overexpressing ETS2 in resting macrophages reproduced the inflammatory state observed in chr21q22-associated diseases, with upregulation of multiple drug targets, including TNF and IL-23. Using a database of cellular signatures7, we identified drugs that might modulate this pathway and validated the potent anti-inflammatory activity of one class of small molecules in vitro and ex vivo. Together, this illustrates the power of functional genomics, applied directly in primary human cells, to identify immune-mediated disease mechanisms and potential therapeutic opportunities.

Genotype × environment interactions in gene regulation and complex traits

Genotype × environment interactions (GxE) have long been recognized as a key mechanism underlying human phenotypic variation. Technological developments over the past 15 years have dramatically expanded our appreciation of the role of GxE in both gene regulation and complex traits. The richness and complexity of these datasets also required parallel efforts to develop robust and sensitive statistical and computational approaches. Although our understanding of the genetic architecture of molecular and complex traits has been maturing, a large proportion of complex trait heritability remains unexplained. Furthermore, there are increasing efforts to characterize the effect of environmental exposure on human health. We therefore review GxE in human gene regulation and complex traits, advocating for a comprehensive approach that jointly considers genetic and environmental factors in human health and disease.

History of tuberculosis disease is associated with genetic regulatory variation in Peruvians

A quarter of humanity is estimated to have been exposed to Mycobacterium tuberculosis (Mtb) with a 5-10% risk of developing tuberculosis (TB) disease. Variability in responses to Mtb infection could be due to host or pathogen heterogeneity. Here, we focused on host genetic variation in a Peruvian population and its associations with gene regulation in monocyte-derived macrophages and dendritic cells (DCs). We recruited former household contacts of TB patients who previously progressed to TB (cases, n = 63) or did not progress to TB (controls, n = 63). Transcriptomic profiling of monocyte-derived DCs and macrophages measured the impact of genetic variants on gene expression by identifying expression quantitative trait loci (eQTL). We identified 330 and 257 eQTL genes in DCs and macrophages (False Discovery Rate (FDR) < 0.05), respectively. Four genes in DCs showed interaction between eQTL variants and TB progression status. The top eQTL interaction for a protein-coding gene was with FAH, the gene encoding fumarylacetoacetate hydrolase, which mediates the last step in mammalian tyrosine catabolism. FAH expression was associated with genetic regulatory variation in cases but not controls. Using public transcriptomic and epigenomic data of Mtb-infected monocyte-derived dendritic cells, we found that Mtb infection results in FAH downregulation and DNA methylation changes in the locus. Overall, this study demonstrates effects of genetic variation on gene expression levels that are dependent on history of infectious disease and highlights a candidate pathogenic mechanism through pathogen-response genes. Furthermore, our results point to tyrosine metabolism and related candidate TB progression pathways for further investigation.

An isoform quantitative trait locus in SBNO2 links genetic susceptibility to Crohn's disease with defective antimicrobial activity

Despite major advances in linking single genetic variants to single causal genes, the significance of genetic variation on transcript-level regulation of expression, transcript-specific functions, and relevance to human disease has been poorly investigated. Strawberry notch homolog 2 (SBNO2) is a candidate gene in a susceptibility locus with different variants associated with Crohn's disease and bone mineral density. The SBNO2 locus is also differentially methylated in Crohn's disease but the functional mechanisms are unknown. Here we show that the isoforms of SBNO2 are differentially regulated by lipopolysaccharide and IL-10. We identify Crohn's disease associated isoform quantitative trait loci that negatively regulate the expression of the noncanonical isoform 2 corresponding with the methylation signals at the isoform 2 promoter in IBD and CD. The two isoforms of SBNO2 drive differential gene networks with isoform 2 dominantly impacting antimicrobial activity in macrophages. Our data highlight the role of isoform quantitative trait loci to understand disease susceptibility and resolve underlying mechanisms of disease.

Characterization of the genetic determinants of context-specific DNA methylation in primary monocytes

To better understand inter-individual variation in sensitivity of DNA methylation (DNAm) to immune activity, we characterized effects of inflammatory stimuli on primary monocyte DNAm (n = 190). We find that monocyte DNAm is site-dependently sensitive to lipopolysaccharide (LPS), with LPS-induced demethylation occurring following hydroxymethylation. We identify 7,359 high-confidence immune-modulated CpGs (imCpGs) that differ in genomic localization and transcription factor usage according to whether they represent a gain or loss in DNAm. Demethylated imCpGs are profoundly enriched for enhancers and colocalize to genes enriched for disease associations, especially cancer. DNAm is age associated, and we find that 24-h LPS exposure triggers approximately 6 months of gain in epigenetic age, directly linking epigenetic aging with innate immune activity. By integrating LPS-induced changes in DNAm with genetic variation, we identify 234 imCpGs under local genetic control. Exploring shared causal loci between LPS-induced DNAm responses and human disease traits highlights examples of disease-associated loci that modulate imCpG formation.

Functional Genomics and Insights into the Pathogenesis and Treatment of Psoriasis

Psoriasis is a lifelong, systemic, immune mediated inflammatory skin condition, affecting 1-3% of the world's population, with an impact on quality of life similar to diseases like cancer or diabetes. Genetics are the single largest risk factor in psoriasis, with Genome-Wide Association (GWAS) studies showing that many psoriasis risk genes lie along the IL-23/Th17 axis. Potential psoriasis risk genes determined through GWAS can be annotated and characterised using functional genomics, allowing the identification of novel drug targets and the repurposing of existing drugs. This review is focused on the IL-23/Th17 axis, providing an insight into key cell types, cytokines, and intracellular signaling pathways involved. This includes examination of currently available biological treatments, time to relapse post drug withdrawal, and rates of primary/secondary drug failure, showing the need for greater understanding of the underlying genetic mechanisms of psoriasis and how they can impact treatment. This could allow for patient stratification towards the treatment most likely to reduce the burden of disease for the longest period possible.

Epigenetic variation impacts individual differences in the transcriptional response to influenza infection

Humans display remarkable interindividual variation in their immune response to identical challenges. Yet, our understanding of the genetic and epigenetic factors contributing to such variation remains limited. Here we performed in-depth genetic, epigenetic and transcriptional profiling on primary macrophages derived from individuals of European and African ancestry before and after infection with influenza A virus. We show that baseline epigenetic profiles are strongly predictive of the transcriptional response to influenza A virus across individuals. Quantitative trait locus (QTL) mapping revealed highly coordinated genetic effects on gene regulation, with many cis-acting genetic variants impacting concomitantly gene expression and multiple epigenetic marks. These data reveal that ancestry-associated differences in the epigenetic landscape can be genetically controlled, even more than gene expression. Lastly, among QTL variants that colocalized with immune-disease loci, only 7% were gene expression QTL, while the remaining genetic variants impact epigenetic marks, stressing the importance of considering molecular phenotypes beyond gene expression in disease-focused studies.

Targeting postsynaptic glutamate receptor scaffolding proteins PSD-95 and PICK1 for obesity treatment

Human genome-wide association studies (GWAS) suggest a functional role for central glutamate receptor signaling and plasticity in body weight regulation. Here, we use UK Biobank GWAS summary statistics of body mass index (BMI) and body fat percentage (BF%) to identify genes encoding proteins known to interact with postsynaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and N-methyl-d-aspartate (NMDA) receptors. Loci in/near discs large homolog 4 (DLG4) and protein interacting with C kinase 1 (PICK1) reached genome-wide significance (P < 5 × 10-8) for BF% and/or BMI. To further evaluate the functional role of postsynaptic density protein-95 (PSD-95; gene name: DLG4) and PICK1 in energy homeostasis, we used dimeric PSD-95/disc large/ZO-1 (PDZ) domain-targeting peptides of PSD-95 and PICK1 to demonstrate that pharmacological inhibition of PSD-95 and PICK1 induces prolonged weight-lowering effects in obese mice. Collectively, these data demonstrate that the glutamate receptor scaffolding proteins, PICK1 and PSD-95, are genetically linked to obesity and that pharmacological targeting of their PDZ domains represents a promising therapeutic avenue for sustained weight loss.

The relationship between extreme inter-individual variation in macrophage gene expression and genetic susceptibility to inflammatory bowel disease

The differentiation of resident intestinal macrophages from blood monocytes depends upon signals from the macrophage colony-stimulating factor receptor (CSF1R). Analysis of genome-wide association studies (GWAS) indicates that dysregulation of macrophage differentiation and response to microorganisms contributes to susceptibility to chronic inflammatory bowel disease (IBD). Here, we analyzed transcriptomic variation in monocyte-derived macrophages (MDM) from affected and unaffected sib pairs/trios from 22 IBD families and 6 healthy controls. Transcriptional network analysis of the data revealed no overall or inter-sib distinction between affected and unaffected individuals in basal gene expression or the temporal response to lipopolysaccharide (LPS). However, the basal or LPS-inducible expression of individual genes varied independently by as much as 100-fold between subjects. Extreme independent variation in the expression of pairs of HLA-associated transcripts (HLA-B/C, HLA-A/F and HLA-DRB1/DRB5) in macrophages was associated with HLA genotype. Correlation analysis indicated the downstream impacts of variation in the immediate early response to LPS. For example, variation in early expression of IL1B was significantly associated with local SNV genotype and with subsequent peak expression of target genes including IL23A, CXCL1, CXCL3, CXCL8 and NLRP3. Similarly, variation in early IFNB1 expression was correlated with subsequent expression of IFN target genes. Our results support the view that gene-specific dysregulation in macrophage adaptation to the intestinal milieu is associated with genetic susceptibility to IBD.

Priority index for critical Covid-19 identifies clinically actionable targets and drugs

While genome-wide studies have identified genomic loci in hosts associated with life-threatening Covid-19 (critical Covid-19), the challenge of resolving these loci hinders further identification of clinically actionable targets and drugs. Building upon our previous success, we here present a priority index solution designed to address this challenge, generating the target and drug resource that consists of two indexes: the target index and the drug index. The primary purpose of the target index is to identify clinically actionable targets by prioritising genes associated with Covid-19. We illustrate the validity of the target index by demonstrating its ability to identify pre-existing Covid-19 phase-III drug targets, with the majority of these targets being found at the leading prioritisation (leading targets). These leading targets have their evolutionary origins in Amniota ('four-leg vertebrates') and are predominantly involved in cytokine-cytokine receptor interactions and JAK-STAT signaling. The drug index highlights opportunities for repurposing clinically approved JAK-STAT inhibitors, either individually or in combination. This proposed strategic focus on the JAK-STAT pathway is supported by the active pursuit of therapeutic agents targeting this pathway in ongoing phase-II/III clinical trials for Covid-19.

Characterization of caffeine response regulatory variants in vascular endothelial cells

Genetic variants in gene regulatory sequences can modify gene expression and mediate the molecular response to environmental stimuli. In addition, genotype-environment interactions (GxE) contribute to complex traits such as cardiovascular disease. Caffeine is the most widely consumed stimulant and is known to produce a vascular response. To investigate GxE for caffeine, we treated vascular endothelial cells with caffeine and used a massively parallel reporter assay to measure allelic effects on gene regulation for over 43,000 genetic variants. We identified 665 variants with allelic effects on gene regulation and 6 variants that regulate the gene expression response to caffeine (GxE, false discovery rate [FDR] < 5%). When overlapping our GxE results with expression quantitative trait loci colocalized with coronary artery disease and hypertension, we dissected their regulatory mechanisms and showed a modulatory role for caffeine. Our results demonstrate that massively parallel reporter assay is a powerful approach to identify and molecularly characterize GxE in the specific context of caffeine consumption.

Whole blood stimulation as a tool for studying the human immune system

The human immune system is best accessible via tissues and organs not requiring major surgical intervention, such as blood. In many circumstances, circulating immune cells correlate with an individual's health state and give insight into physiological and pathophysiological processes. Stimulating whole blood ex vivo is a powerful tool to investigate immune responses. In the context of clinical research, the applications of whole blood stimulation include host immunity, disease characterization, diagnosis, treatment, and drug development. Here, we summarize different setups and readouts of whole blood assays and discuss applications for preclinical research and clinical practice. Finally, we propose combining whole blood stimulation with high-throughput technologies, such as single-cell RNA-sequencing, to comprehensively analyze the human immune system for the identification of biomarkers, therapeutic interventions as well as companion diagnostics.

A common NFKB1 variant detected through antibody analysis in UK Biobank predicts risk of infection and allergy

Infectious agents contribute significantly to the global burden of diseases through both acute infection and their chronic sequelae. We leveraged the UK Biobank to identify genetic loci that influence humoral immune response to multiple infections. From 45 genome-wide association studies in 9,611 participants from UK Biobank, we identified NFKB1 as a locus associated with quantitative antibody responses to multiple pathogens, including those from the herpes, retro-, and polyoma-virus families. An insertion-deletion variant thought to affect NFKB1 expression (rs28362491), was mapped as the likely causal variant and could play a key role in regulation of the immune response. Using 121 infection- and inflammation-related traits in 487,297 UK Biobank participants, we show that the deletion allele was associated with an increased risk of infection from diverse pathogens but had a protective effect against allergic disease. We propose that altered expression of NFKB1, as a result of the deletion, modulates hematopoietic pathways and likely impacts cell survival, antibody production, and inflammation. Taken together, we show that disruptions to the tightly regulated immune processes may tip the balance between exacerbated immune responses and allergy, or increased risk of infection and impaired resolution of inflammation.

Variants at the Interleukin 1 Gene Locus and Pericarditis

Importance

Recurrent pericarditis is a treatment challenge and often a debilitating condition. Drugs inhibiting interleukin 1 cytokines are a promising new treatment option, but their use is based on scarce biological evidence and clinical trials of modest sizes, and the contributions of innate and adaptive immune processes to the pathophysiology are incompletely understood.

Objective

To use human genomics, transcriptomics, and proteomics to shed light on the pathogenesis of pericarditis.

Design, Setting, and Participants

This was a meta-analysis of genome-wide association studies of pericarditis from 5 countries. Associations were examined between the pericarditis-associated variants and pericarditis subtypes (including recurrent pericarditis) and secondary phenotypes. To explore mechanisms, associations with messenger RNA expression (cis-eQTL), plasma protein levels (pQTL), and CpG methylation of DNA (ASM-QTL) were assessed. Data from Iceland (deCODE genetics, 1983-2020), Denmark (Copenhagen Hospital Biobank/Danish Blood Donor Study, 1977-2022), the UK (UK Biobank, 1953-2021), the US (Intermountain, 1996-2022), and Finland (FinnGen, 1970-2022) were included. Data were analyzed from September 2022 to August 2023.

Exposure

Genotype.

Main Outcomes and Measures

Pericarditis.

Results

In this genome-wide association study of 4894 individuals with pericarditis (mean [SD] age at diagnosis, 51.4 [17.9] years, 2734 [67.6%] male, excluding the FinnGen cohort), associations were identified with 2 independent common intergenic variants at the interleukin 1 locus on chromosome 2q14. The lead variant was rs12992780 (T) (effect allele frequency [EAF], 31%-40%; odds ratio [OR], 0.83; 95% CI, 0.79-0.87; P = 6.67 × 10-16), downstream of IL1B and the secondary variant rs7575402 (A or T) (EAF, 45%-55%; adjusted OR, 0.89; 95% CI, 0.85-0.93; adjusted P = 9.6 × 10-8). The lead variant rs12992780 had a smaller odds ratio for recurrent pericarditis (0.76) than the acute form (0.86) (P for heterogeneity = .03) and rs7575402 was associated with CpG methylation overlapping binding sites of 4 transcription factors known to regulate interleukin 1 production: PU.1 (encoded by SPI1), STAT1, STAT3, and CCAAT/enhancer-binding protein β (encoded by CEBPB).

Conclusions and Relevance

This study found an association between pericarditis and 2 independent sequence variants at the interleukin 1 gene locus. This finding has the potential to contribute to development of more targeted and personalized therapy of pericarditis with interleukin 1-blocking drugs.

PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs

Expression quantitative trait loci (eQTL) offer insights into the regulatory mechanisms of trait-associated variants, but their effects often rely on contexts that are unknown or unmeasured. We introduce PICALO, a method for hidden variable inference of eQTL contexts. PICALO identifies and disentangles technical from biological context in heterogeneous blood and brain bulk eQTL datasets. These contexts are biologically informative and reproducible, outperforming cell counts or expression-based principal components. Furthermore, we show that RNA quality and cell type proportions interact with thousands of eQTLs. Knowledge of hidden eQTL contexts may aid in the inference of functional mechanisms underlying disease variants.